Evaluation of efficacy of epilepsy therapy

ABSTRACT

A method of detecting an improvement in a seizure condition of a patient includes identifying a first EEG synchronization of the seizure condition of the patient; applying a therapy configured to improve the seizure condition of the patient; and identifying a second EEG synchronization of the seizure condition of the patient subsequent to application of the therapy, wherein an improvement of the seizure condition of the patient is demonstrated by a reduced EEG synchronization of the patient such that the second EEG synchronization is less than the first EEG synchronization.

CROSS-REFERENCE TO RELATED PATENT APPLICATIONS

This application claims priority to and the benefit of U.S. ProvisionalApplication No. 62/150,773, filed Apr. 21, 2015, which is herebyincorporated by reference herein in its entirety.

BACKGROUND

The present disclosure relates generally to the field of therapy for areduction in severity of seizures in patients.

Approximately 3 million people in the US have epilepsy, of whom 10% to20% have a form of epilepsy that is not well controlled withanti-epileptic drugs (i.e. drug refractory). Patients with drugrefractory epilepsy may seek alternative solutions such as surgicalresection, ketogenic diet and neuromodulation. Vagus nerve stimulation,or VNS Therapy®, is an FDA approved neuromodulation treatment that maybe used as an adjunctive therapy to pharmacology and has beendemonstrated to reduce seizure frequency in a multicenter, randomizedcontrolled trial as well as in subsequent single center trials. Acurrent, commercially available VNS Therapy utilizes an open loopstimulation paradigm (i.e. the stimulation is delivered using apredetermined duty cycle (i.e. “ON” and “OFF” times)) with parametersthat are physician programmable (i.e. current amplitude, pulse width,frequency). In addition, VNS Therapy provides patients and caregiverswith an opportunity to administer stimulation on demand during or justprior to a seizure via the use of a portable magnet. The use of such amagnet to manually activate on-demand stimulation has been reported toterminate or in some cases, reduce the severity of seizures. However,not all patients have the ability to apply the magnet before the onsetof a seizure and/or experience an aura (i.e., an early warning of animpending clinical seizure).

SUMMARY

One embodiment relates to a method of detecting an improvement in aseizure condition of a patient. The method includes identifying a firstEEG synchronization of the seizure condition of the patient; applying atherapy configured to improve the seizure condition of the patient; andidentifying a second EEG synchronization of the seizure condition of thepatient subsequent to application of the therapy, wherein an improvementof the seizure condition of the patient is demonstrated by a reduced EEGsynchronization of the patient such that the second EEG synchronizationis less than the first EEG synchronization.

Another embodiment relates to a device configured to detect animprovement in a seizure condition of a patient. The device includes atleast one EEG sensor configured to generate EEG data and a therapyanalysis device configured to receive the EEG data. The therapy analysisdevice is configured to evaluate an effect of a therapy. The evaluationincludes identifying a first EEG synchronization of the seizurecondition of the patient, applying a therapy configured to improve theseizure condition of the patient, and identifying a second EEGsynchronization of the seizure condition of the patient subsequent toapplication of the therapy, wherein an improvement of the seizurecondition of the patient is demonstrated by a reduced EEGsynchronization of the patient such that the second EEG synchronizationis less than the first EEG synchronization.

Yet another embodiment relates to a method of detecting an improvementin a seizure condition of a patient. The method includes identifying afirst EEG synchronization of the seizure condition of the patient byextracting first maximum wavelet coefficients in a first plurality ofepochs and a plurality of frequency bands for a plurality of EEGsensors; computing a first global spatial synchronization for eachfrequency band across the plurality of EEG sensors using the firstmaximum wavelet coefficients; and estimating a first synchronizabilityindex for each of the plurality of EEG sensors using the first globalspatial synchronization. The method further includes applyingstimulation to a vagus nerve of the patient using a vagus nervestimulation device configured to improve the seizure condition of thepatient. The method further includes identifying a second EEGsynchronization of the seizure condition of the patient subsequent toapplication of the stimulation by extracting second maximum waveletcoefficients in a second plurality of epochs and the plurality offrequency bands for the plurality of EEG sensors; computing a secondglobal spatial synchronization for each frequency band across theplurality of EEG sensors using the second maximum wavelet coefficients;and estimating a second synchronizability index for each of theplurality of EEG sensors using the second global spatialsynchronization. The method further includes generating an indication ofan efficacy of the stimulation to the vagus nerve using at least one ofthe second synchronizability index or a comparison of the secondsynchronizability index to the first synchronizability index.

The foregoing summary is illustrative only and is not intended to be inany way limiting. In addition to the illustrative aspects, embodiments,and features described above, further aspects, embodiments, and featureswill become apparent by reference to the drawings and the followingdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

Further features, characteristics and advantages of the presentdisclosure will become apparent to a person of ordinary skill in the artfrom the following detailed description of embodiments of the presentdisclosure, made with reference to the drawings annexed, in which likereference characters refer to like elements, and in which:

FIG. 1 is an illustration of a nerve stimulation device having aneurostimulator and a lead, according to an example embodiment;

FIG. 2 is schematic block diagram of the nerve stimulation device ofFIG. 1, according to an example embodiment;

FIG. 3 is an illustration of the nerve stimulation device of FIG. 1implanted within a patient to provide vagus nerve stimulation, accordingto an example embodiment;

FIGS. 4A-4B are diagrams showing electrode placement for collecting EEGsignals with a left side view and a top view, respectively, according toan example embodiment;

FIG. 5 is a schematic block diagram of a therapy analysis system,according to an example embodiment;

FIGS. 6A-6B are flow charts of methods of measuring a severity of aseizure and an efficacy of VNS therapy, according to an exampleembodiment;

FIG. 7 is a flow chart of a method of analyzing ECG data according toFIG. 6, according to an example embodiment;

FIG. 8A is an ECG signal versus time, according to an exampleembodiment;

FIG. 8B is an instantaneous heart rate (IHR) verses time correspondingto the ECG signal of FIG. 8A, before and after despiking, according toan example embodiment;

FIG. 8C is a phase-space plot showing a phase space despiking approachof the ECG signal of FIG. 8A, according to an example embodiment;

FIG. 8D is a smoothed version of the IHR of FIG. 8B, according to anexample embodiment;

FIG. 9 is a flow chart of a method of determining a synchronizability ofeach EEG electrode and a number of synchronized EEG electrodes accordingto FIG. 6, according to an example embodiment;

FIG. 10 is the method of FIG. 9, shown in images, according to anexample embodiment;

FIG. 11A is a raw EEG recording, according to an example embodiment;

FIG. 11B is a graph of the maximum wavelet coefficients (MaxWC) in the δfrequency sub-band, according to an example embodiment;

FIG. 11C is an synchronizability index in the δ frequency sub-band,according to an example embodiment;

FIGS. 12A, 12C, 12E and 12G are images of a wavelet coefficient changeas a result of seizure before VNS therapy, according to an exampleembodiment;

FIGS. 12B, 12D, 12F and 12H are images of a wavelet coefficient changeas a result of seizure after VNS therapy, according to an exampleembodiment;

FIG. 13 is a table showing a summary of patients' characteristics;

FIG. 14A is a graph displaying a magnitude and duration of a heart rateincrease during a seizure before VNS therapy, according to an exampleembodiment;

FIG. 14B is a graph displaying a magnitude and duration of a heart rateincrease during a seizure after VNS therapy, according to an exampleembodiment;

FIG. 15A is the synchronizability index for all frequency sub bandsbefore VNS therapy, according to an example embodiment;

FIG. 15B is the synchronizability index for all frequency sub bandsafter VNS therapy, according to an example embodiment;

FIG. 16A is the synchronizability index for the δ frequency sub-bandshowing the synchronized electrodes during a seizure before VNS therapy,according to an example embodiment;

FIG. 16B is the synchronizability index for the δ frequency sub-bandshowing the synchronized electrodes during a seizure after VNS therapy,according to an example embodiment;

FIG. 17 is a three-dimensional view of a seizure severity feature space,according to an example embodiment;

FIG. 18A is a table showing the statistical data as a result of thestudy performed;

FIG. 18B is a table showing the classification performance in predictingseizures according to the results of the study; and

FIG. 18C is a table showing the classification of seizures that receivedVNS therapy according to the results of the study.

DETAILED DESCRIPTION

Various aspects of the disclosure will now be described with regard tocertain examples and embodiments, which are intended to illustrate butnot to limit the disclosure. Nothing in this disclosure is intended toimply that any particular feature or characteristic of the disclosedembodiments is essential. The scope of protection is defined by theclaims that follow this description and not by any particular embodimentdescribed herein. Before turning to the figures, which illustrateexample embodiments in detail, it should be understood that theapplication is not limited to the details or methodology set forth inthe description or illustrated in the figures. It should also beunderstood that the terminology is for the purpose of description onlyand should not be regarded as limiting.

The Cyberonics AspireSR™ neurostimulator incorporates a closed-loopsystem that detects potential seizure events associated with heart rateincrease and automates the magnet activation and thereby deliversstimulation automatically. This feature of the device is called theautomated magnet mode (AMM) feature and represents one computationallyefficient means of achieving closed-loop neuromodulation. The seizuredetection algorithm within AspireSR leverages that seizure originationand/or propagation may functionally impair neural circuits involved insympathetic cardiovascular regulation in the mesial temporal lobestructures that can manifest as tachycardia during a seizure (ictalstate).

Recently, a prospective, multi-center clinical trial in an epilepsymonitoring unit (EMU) environment was conducted where the detectionperformance of this algorithm was tested against the visual inspectionof EEG. This clinical trial (NCT01325623) met its primary endpoint ofachieving greater than 80% sensitivity in seizure detection.

The clinical benefit of open-loop VNS have been shown in severalstudies. One metric of assessing clinical benefit of VNS or any othertherapy in epilepsy has been seizure frequency reduction as measuredusing seizure diaries. However, as discussed above, these diaries havebeen shown to be inaccurate and subjective. In addition, severalqualitative scales that assess quality of life and severity of theseizure disorder have been suggested; however, they are seldom used inclinical practice or in clinical studies as a primary endpoint forevaluating new therapies for epilepsy. This has motivated the need toidentify reliable and measurable biomarkers of efficacy of epilepsytherapy in general, and VNS in particular. A few researchers haveexamined the effects of VNS using objective measures of EEG(electroencephalographic) activity.

A study in this area by Hammond et al., titled “Electrophysiologicalstudies of cervical vagus nerve stimulation in humans: I. EEG effects”proved that VNS may interrupt ongoing ictal EEG activity. Then, Koo etal. revealed progressive decrease in duration and frequency of spikesand wave activity on interracial EEG signals by long term monitoring theeffects of chronic VNS in “EEG changes with vagus nerve stimulation.”Kuba et al. showed that acute stimulation of vagal nerves reduces thenumber of interictal epileptiform discharges where the reduction is mostprominent during the stimulation periods in “Effect of vagal nervestimulation on interictal epileptiform discharges: a scalp EEG study.”Marrosu et al. have used EEG frequency profile in a study to determinethe effect of chronic VNS therapy, as described in “Increase in 20-50 Hz(gamma frequencies) power spectrum and synchronization after chronicvagal nerve stimulation.” Marrosu et al. evaluated the power spectrumdensity and synchronization from EEGs before and after VNS therapy andidentified decreases in the synchronization in theta band and increasesin power spectrum and synchronization in gamma (20-50 Hz) after VNStherapy.

Later in another study by Fraschini et al., titled “VNS induceddesynchronization in gamma bands correlates with positive clinicaloutcome in temporal lobe pharmacoresistant epilepsy,” used phase lagindex to compare the EEG synchronization in responders andnon-responders to chronic VNS therapy. Fraschini et al. found astatistical decrease in de-synchronization in gamma band after fiveyears from VNS surgery, in responders to the VNS therapy, while theother frequency bands do not show significant variation. Vos et al. useda pair wise derived brain symmetry index (pdBSI) to indicate apossibility of predicting a response to VNS therapy from interictal EEGbefore the onset of VNS therapy, as described in “Predicting success ofvagus nerve stimulation (VNS) from interictal EEG.” Vos et al. realized,on average, lower pdBSI values in responders than non-responders in allfour frequency sub-bands of δ, θ, α, and β, where the average pdBSI issignificantly discriminating between responders and non-responders inthe two frequency sub-bands of θ and α. Vos et al. concluded that pdBSIcould be used as a feature to predict the response to VNS therapy as theresponders have, on average, less asymmetric spectral characteristics ofthe interictal EEG than non-responders.

Vagus nerve stimulation (VNS) therapy is an adjunctive therapy forpatients with medically refractory epilepsy. One of the primary metricsused to assess a response to any treatment for epilepsy is seizurefrequency reduction. This is generally measured using subjectivepatient-reported outcomes, such as seizure diaries. However, it has beenshown that self-reporting of seizure frequency is severely inaccurate.In addition to seizure frequency, reduction in seizure severity isclinically meaningful to patients and can be measured objectively.Analysis of electro-encephalographic (EEG) and electro-cardiographic(ECG/EKG) signals have revealed that seizures are accompanied by spatialsynchronization of EEG and increased heart rates that may persist forseveral minutes to hours after the seizure, increasing the likelihood ofSudden Unexplained Death in Epilepsy (SUDEP). In view of thisunderstanding, it may be possible to show that the automated delivery ofVNS at a time of seizure onset (i.e. closed-loop) reduces a severity ofseizures in patients with epilepsy by showing a reduction in EEG spatialsynchronization as well as a reduction in the duration and magnitude ofan accompanying heart rate increase.

From a cardiovascular perspective, ictal discharges that occur in orpropagate to key brain structures that regulate autonomic function canlead to increased sympathetic outflows, impacting autonomic function.This increased sympathetic tone to the heart has been measured usingheart rate variability (HRV) analysis. HRV markers of sympathetic tonedominance have been shown to be strong and independent predictors ofmortality including sudden cardiac death in patients after an acutemyocardial infarction as well as in those with heart failure orcardiomyopathy. This also implies that a therapy that is effective mayalter HRV. Recently, reduction in T-wave alternans (TWA) has been shownto be an indicator of VNS therapy in patients with epilepsy. However,the signal-to-noise ratio for T-wave measurements is significantly lowerthan R-wave and is more prone to variations due to bodyposition/location than R-waves. This makes TWA a difficult marker tomonitor chronically to study the effects of VNS. It has been shown thatgeneralized seizures appear to have higher heart rate increases thatlast for a longer time than complex partial seizures, suggesting thatanother metric of evaluating the effectiveness of a therapy may be tostudy its effect in reducing ictal tachycardia increase and duration.Moreover, from a signal processing perspective, measuring heart rate(via R-wave detection) is significantly easier than other morphologicalparameters of ECG.

According to some embodiments of the present disclosure, the performanceof the AMM feature in reducing the severity of seizures in patients withepilepsy can be evaluated by using a combination of features obtainedfrom continuous observational video electroencephalography (vEEG) andelectrocardiogram (ECG) data around ictal events. These events may occurduring a epilepsy monitoring unit (EMU) evaluation (e.g., 3 to 5 days).Performance of the AMM feature may be measured by the ability todiscriminate (classify) seizures in patients prior to using VNS therapyfrom the ones that occur following AMM-based VNS therapy. In order toachieve this, in some embodiments, three features may be utilized 1)heart rate amount (e.g., percentage, peak heart rate increase) changeduring tachycardia, 2) tachycardia duration (e.g., in seconds) and 3)number of EEG electrodes that contribute the most to a global spatialsynchronization that accompany seizures, to show that application ofAMM-based VNS therapy reduces the ictal spatial synchronization (asmeasured by EEG-based features) and the autonomic effects of seizures(as measured by ECG-based features).

Referring to the Figures generally, the present disclosure relates toapparatuses, systems, and methods for verifying the efficacy of VNStherapy to reduce the severity of seizures for patients. Seizures arecaused by problems in electrical signaling of the brain. Individuals whosuffer from reoccurring seizures are generally diagnosed with epilepsy.Seizures may affect a specific area of the brain (e.g., the lefthemisphere) or may be more widespread in the brain and affect a largerarea. With VNS, the electrical energy that is discharged disrupts theabnormal brain activity, which may decrease seizure activity andtherefore severity.

Therefore, VNS may be used for the treatment of seizures. During VNS,stimulation may be directly and/or indirectly applied to the vagus nervewith a lead having an electrode powered by an implantableneurostimulator. By way of example, the electrode (e.g., a cuff-typeelectrode, a helical-type electrode, etc.) may be attached to theexterior of the vagus nerve (e.g., at the cervical level of the vagusnerve, etc.) to provide VNS directly to the vagus nerve.

However, VNS may not reduce the severity of seizures for some patients.In fact, in some instances, VNS may adversely affect some patients(e.g., leading to worse health issues, high medical costs forineffective medical procedures, etc.). Thus, VNS can be evaluated todetermine if the therapy is effective in reducing the severity ofseizures. By determining the effectiveness of the therapy, a physicianmay alter parameters of the VNS to obtain a higher efficacy oftreatment.

According to an exemplary embodiment, the apparatuses, systems, andmethods of the present disclosure are used to determine the efficacy ofapplying VNS to patients that have reoccurring seizures. In someembodiments, this may be done by analyzing a percent of heart ratechange during a seizure, a duration of heart rate change during aseizure, and/or a measure of spatial synchronization of EEG during aseizure. These measurements may be used separately or in conjunctionwith one another to determine efficacy or severity of a seizure. Thisinformation can be compared for an individual before and after receivingautomatic VNS. Such a determination may lead to reduced severity ofseizures for patients, as well as provide physicians with informationrelating to parts of the brain that are still being affected by theseizure.

Referring now to FIGS. 1-3, an implantable stimulation device, shown asvagus nerve stimulator 10, may be implanted within a person, shown aspatient 100, to provide VNS.

However, other forms of neuromodulation may be used as well and areintended to fall within the spirit and scope of the present disclosure.Additionally, the efficacy evaluation techniques presented in thepresent disclosure are equally applicable to other forms of epilepsytherapy as well, such as drug-based therapies. Thus, while variousembodiments herein are discussed with respect to neuromodulation therapysuch as VNS, the techniques discussed herein are contemplated for usewith any epilepsy therapy, and use of the techniques with all suchtherapies is within the scope of the present disclosure. Such a vagusnerve stimulator 10 may be adapted for use in managing severity ofseizures through therapeutic vagal stimulation. According to anexemplary embodiment, the vagus nerve stimulator 10 operates underseveral mechanisms of action. These mechanisms may include disruptingabnormal brain activity through stimulation. More importantly, thestimulation provided by the vagus nerve stimulator 10 may be triggeredby an automated magnet mode (AMM) that detects potential seizure events,which may be associated with heart rate increase, and automates a magnetactivation and thereby delivers stimulation automatically.

As shown in FIGS. 1-2, the vagus nerve stimulator 10 includes animplantable neurostimulator, shown as neurostimulator 20 and a therapylead, shown as lead 30. According to an exemplary embodiment, theneurostimulator 20 is configured to generate an electrical signal thatthe lead 30 delivers to a desired location (e.g., the vagus nerve,etc.). As shown in FIG. 1, the neurostimulator 20 includes a housing,shown as hermetically sealed housing 22. According to an exemplaryembodiment, the hermetically sealed housing 22 is manufactured from abiocompatible, implantation-safe material (e.g., titanium, etc.). Theneurostimulator 20 includes a connector cover, shown as header 24,coupled to the hermetically sealed housing 22. The header 24 isportioned to enclose a connection interface, shown as receptacle 26.While vagus nerve stimulation 10 is illustrated as an implantableneurostimulator, in other embodiments, a non-implantable or externalneurostimulation device could be used.

The lead 30 includes a wire, shown as lead wire 32. In one embodiment,the lead wire 32 includes a silicone-insulated alloy conductor material.The lead 30 includes a connector, shown as a lead connector 34,positioned on a proximal end of the lead wire 32. As shown in FIG. 1,the lead connector 34 transitions from an insulated electrical lead bodyto a connection interface, shown as connector pin 36 (e.g., a metalconnector pin, etc.). In one embodiment, the lead connector 34 ismanufactured using silicone and the connector pin 36 is made ofstainless steel, although other suitable materials may be used as well.According to an exemplary embodiment, the connector pin 36 is configuredto be received by the receptacle 26 of the neurostimulator 20 to couplethe lead 30 thereto. During implantation, the connector pin 36 is guidedthrough the receptacle 26 into the header 24 and securely fastened inplace using a fastener, shown as set screws 28, thereby electricallycoupling the lead 30 to the neurostimulator 20. In one embodiment, theheader 24 encloses the receptacle 26 into which a single connector pin36 for the lead 30 may be received. In other embodiments, two or morereceptacles 26 may also be provided, to couple additional leads 30 tothe neurostimulator 20.

As shown in FIGS. 1-2, the lead 30 includes a simulation element, shownas electrode 38. As shown in FIG. 1, the electrode 38 is positioned on adistal end of the lead wire 32. According to the exemplary embodimentshown in FIG. 1, the electrode 38 includes a cuff-type electrode 40. Inother embodiments, the electrode 38 includes another type of electrode(e.g., a pig-tail or helical electrode, etc.). In some embodiments,another type of lead is used (e.g., a stent, a pig-tail lead, apreformed lead, etc.). According to an exemplary embodiment, theelectrode 38 is configured to deliver an electrical signal from theneurostimulator 20 to a desired location (e.g., the vagus nerve, etc.).

As shown in FIGS. 1-2, the neurostimulator 20 includes (e.g., containedwithin the hermetically sealed housing 22, etc.) electronic circuitry,shown as control circuit 50, an energy storage device, shown as battery70, a communications interface, shown as communications interface 72,and a pulse generator, shown as electric pulse generator 74. The battery70 is configured to power the neurostimulator 20 (e.g., the controlcircuit 50, the electric pulse generator 74, the communicationsinterface 72, etc.). In some embodiments, the battery 70 includes alithium carbon monoflouride battery. In other embodiments, the battery70 includes another type of battery (e.g., a lithium-ion battery, anickel-metal hydride battery, etc.). According to an exemplaryembodiment, the communications interface 72 is configured to provideremote access to the operation of the neurostimulator 20 using anexternal programmer, a simple patient magnet, and/or an electromagneticcontroller. In one embodiment, the communications interface 72 includesa Reed circuit. In some embodiments, the communications interface 72includes a transceiver that remotely communicates with the externalprogrammer using a wireless communication protocol (e.g., radiofrequency signals, Bluetooth, etc.) to receive programming instructionsand/or transmit telemetry information to the external programmer orother external device. In some embodiments, other components, such as anintegrated heart rate sensor, may be integrated within theneurostimulator 20.

According to an exemplary embodiment, the control circuit 50 isconfigured to control the electric pulse generator 74 to generateelectric pulses to be delivered by the lead 30 (e.g., the electrode 38,etc.) to provide stimulation to a desired location (e.g., the vagusnerve, etc.). Thereby, the neurostimulator 20 may deliver VNS undercontrol of the control circuit 50 based on stored stimulation parametersthat are programmable (e.g., by a physician, by the manufacturer, etc.).Each stimulation parameter may be independently programmed to define thecharacteristics of the cycles of therapeutic stimulation and inhibitionto ensure optimal stimulation for a patient. The programmablestimulation parameters may include output current, signal frequency,pulse width, signal ON time, signal OFF time, magnet activation (e.g.,for VNS specifically triggered by magnet mode, etc.), and/or resetparameters. Other programmable parameters are possible. In addition,sets or “profiles” of preselected stimulation parameters may be providedto physicians with the external programmer and fine-tuned to a patient'sphysiological requirements prior to being programmed into theneurostimulator 20.

The neurostimulator 20 may be interrogated prior to implantation andthroughout the therapeutic period with a healthcare provider-operableexternal programmer and programming wand for checking proper operation,downloading recorded data, diagnosing problems, and programmingoperational parameters. In some embodiments, use of the externalprogrammer is restricted to healthcare providers, while more limitedmanual control is provided to the patient through magnet mode. In oneembodiment, the external programmer executes application softwarespecially designed to interrogate the neurostimulator 20. Theprogramming computer may interface to the programming wand through astandardized or proprietary wired or wireless data connection. Otherconfigurations and combinations of external programmer, programmingwand, and/or application software are possible.

As shown in FIG. 2, the control circuit 50 of the vagus nerve stimulator10 includes a processing circuit 52. The processing circuit 52 includesa processor 54 and a memory 56. The processor 54 may be implemented as ageneral-purpose processor, an application specific integrated circuit(ASIC), one or more field programmable gate arrays (FPGAs), a digitalsignal processor (DSP), a group of processing components, or othersuitable electronic processing components. The memory 56 (e.g., RAM,ROM, Flash Memory, etc.) may store data and/or computer code forfacilitating the various processes described herein. Thus, the memory 56may be communicably connected to the processor 54 and provide computercode or instructions to the processor 54 for executing the processesdescribed in regard to the vagus nerve stimulator 10 herein. Moreover,the memory 56 may be or include tangible, non-transient volatile memoryor non-volatile memory. In some embodiments, the memory 56 may includedatabase components, object code components, script components, or anyother type of information structure for supporting the variousactivities and information structures described herein.

The memory 56 may include various circuits for completing processesdescribed herein. More particularly, the memory 56 includes circuitsconfigured to control operation of the vagus nerve stimulator 10 toprovide VNS. The memory 56 may store instructions that operate the vagusnerve stimulator 10 according to stored stimulation parameters andtiming cycles (e.g., a predefined stimulation protocol, automated magnetmode, etc.). For example, the memory 56 may include a voltage regulatorthat regulates system power, a stimulation manager that controls theoverall pulse generator function, an input circuit that receives andimplements programming commands from the external programmer or otherexternal source, and/or data storage that collects and stores telemetryinformation, among other possible circuits that perform additional oralternative functions. While various circuits with particularfunctionality may be used, it will be understood that the memory 56 mayinclude any number of circuits for completing the functions describedherein. For example, the activities of multiple circuits may be combinedas a single circuit; additional circuits with additional functionalitymay be included. Further, it will be understood that the processingcircuit 52 of the vagus nerve stimulator 10 may further control otherprocesses beyond the scope of the present disclosure.

According to the exemplary embodiment shown in FIG. 3, theneurostimulator 20 is implanted into the right or left pectoral regionof a patient 100. Generally, the neurostimulator 20 is implanted on thesame side (ipsilateral) of the patient's body as the vagus nerve 120 tobe stimulated (e.g., right or left vagus nerve 120, etc.), althoughother neurostimulator-vagus nerve configurations, includingcontra-lateral and bi-lateral, are possible. As shown in FIG. 3, thecuff-type electrode 40 is implanted on the main trunk 122 of the vagusnerve 120 about halfway between the clavicle 140a-b and the mastoidprocess (e.g., at the cervical level, etc.). The lead 30 and cuff-typeelectrodes 40 may be implanted by first exposing the carotid sheath andchosen vagus nerve 120 through a latero-cervical incision on theipsilateral side of the neck 102 of the patient 100. The cuff-typeelectrodes 40 are then placed onto the exposed nerve sheath andtethered. A subcutaneous tunnel is formed between the respectiveimplantation site of the neurostimulator 20 and cuff-type electrode 40,through which the lead 30 is guided to the neurostimulator 20 andsecurely connected. Such an implantation requires an invasive surgicalprocedure under general anesthesia.

Once implantation of the vagus nerve stimulator 10 is completed, theneurostimulator 20 may provide VNS directly to the main truck 122 of thevagus nerve 120 with the cuff-type electrode 40. The stimulationproduces action potentials in the underlying nerves that propagatebi-directionally, in some embodiments. Both sympathetic andparasympathetic nerve fibers may be stimulated through the cuff-typeelectrode 40 of the vagus nerve stimulator 10. Stimulation of thecervical vagus nerve 120 results in propagation of action potentials inboth afferent and efferent directions from the site of stimulation torestore autonomic balance. Afferent action potentials propagate towardthe parasympathetic nervous system's origin in the medulla in thenucleus ambiguus, nucleus tractus solitarius, and the dorsal motornucleus, as well as towards the sympathetic nervous system's origin inthe intermediolateral cell column of the spinal cord. Efferent actionpotentials propagate toward the heart 110 to activate the components ofthe heart's intrinsic nervous system. Either the left or right vagusnerve 120 may be stimulated by the vagus nerve stimulator 10, althoughstimulation of the right vagus nerve 120 has a moderately strongereffect on heart rate (e.g., on the order of approximately 20% stronger)than left vagus nerve 120 stimulation at the same parametric levels.

At the cervical level, the vagus nerve 120 contains afferent fibers butalso efferent ones innervating most of the intra-thoracic and abdominalorgans as well as the laryngeal area through the recurrent laryngealnerve which is included with the vagus nerve 120 in the neck 102.Stimulating the vagus nerve 120 in the cervical region may lead toadverse effects, related to large innervated areas, including cough,voice alteration/hoarseness, pain, dyspnea, nausea, etc.

While a patient may be receiving VNS therapy to reduce severity andfrequency of seizures, the current methods of quantifying the reductionare generally not very accurate. Now referring to FIGS. 4A and 4B,diagrams showing electrode placement for collecting EEG signals areshown, according to an example embodiment. To determine the severity ofa seizure, EEG data can be collected and analyzed to determine alocation and amount of electrical activity where a seizure is occurringin the brain. To collect EEG data, an electrode array may be placed onthe scalp of a patient. For example, the 10-20 system may be used todetermine the placement of the electrodes. Combinations of letters andnumbers are used to distinguish the electrodes from one another based ona location of the electrode. In some embodiments, 19-25 electrodechannels may be used. However, in other embodiments, more or lesselectrodes may be used. Parameters that can be measured from the EEGelectrodes may include phase, frequency, amplitude, coupling ofchannels, etc. Alternatively, intracranial electrodes may also be usedto obtain EEG data.

Referring now to FIG. 5, a therapy analysis system 200 may be used tofacilitate assessing an efficacy in reducing seizure severity in apatient using vagus nerve stimulation therapy (e.g., determining if apatient is positively responding to VNS). As shown in FIG. 5, thetherapy analysis system 200 includes a user input/output (I/O) device210, a stimulation device 220, one or more sensors 230, and a processingcircuit 252. While the device is discussed with respect to analyzing andmonitoring VNS therapy, the device may additionally or alternatively beused to assess an efficacy in reducing seizure severity in a patientusing other forms of therapy. For example, the analysis system 200 mayassess an efficacy in reducing seizure severity in a patient using adrug therapy, or a combination of VNS and drug therapy.

The user I/O device 210 may enable a user of the therapy analysis system200 to communicate with the therapy analysis system 200 and othercomponents thereof (e.g., the stimulation device 220, etc.). In someembodiments, the user I/O device 210 is communicably coupled to thetherapy analysis system 200 via a wireless communication protocol (e.g.,Bluetooth, Zigbee, Wi-Fi, radio, cellular, etc.). In some embodiments,the user I/O device 210 is directly communicably coupled to the VNSanalysis system 200 (e.g., with a wired connection, etc.). The user I/Odevice 210 may include an input device and/or a display device. Theinput device may be configured to allow a user to control the VNSanalysis system 200 and/or input various parameters (e.g., stimulationparameters, etc.). The input device may include, but is not limited to,a keyboard, a mouse, a touchscreen device, one or more buttons andswitches, voice command receivers, a portable device (e.g., a smartphone, a tablet, a laptop, etc.), etc. The display device may beconfigured to provide a graphical user interface (GUI) to the user ofthe therapy analysis system 200. The display device may include, but isnot limited to, a touchscreen display, a projector and projectionscreen, a monitor or television (e.g., a LCD, LED, plasma, DLP, etc.),augmented reality glasses, a portable device (e.g., a smartphone,tablet, laptop, etc.), and/or any other known display devices that canprovide a GUI.

The stimulation device 220 may be configured to provide stimulation(e.g., acute, temporary, etc.) during or before a seizure. In oneembodiment, the stimulation device 220 includes a lead (e.g., the lead30, etc.) having at least one electrode (e.g., the electrodes 38, etc.)configured to be positioned proximate to a portion of the vagus nerve120 (e.g., the cardiac fascicles 126 that branch from the vagus nerve120, etc.). In another embodiment, the stimulation device 220 includesan external stimulation device configured to be positioned outside thebody of the patient to provide the stimulation through the skin of thepatient. In one embodiment, the external stimulation device includes anauricular stimulation device configured to provide auricular stimulationaround and/or near an ear of the patient. In other embodiments, theexternal stimulation device includes another type of stimulation deviceconfigured to provide stimulation to another external area of thepatient (e.g., the chest, the back, the neck, etc.). In still otherembodiments, the stimulation device 220 includes a cuff-type electrode(e.g., the cuff-type electrode 40, etc.) or another type of electrodetemporarily implanted onto the vagus nerve 120 and configured to providestimulation directly to the vagus nerve 120 (e.g., VNS, etc.).

The sensors 230 may be configured to acquire response data of a patienthaving a seizure to facilitate monitoring a physiological response ofthe patient to VNS therapy during a seizure. The sensors 230 mayfacilitate monitoring one or more physiological responses of the patientincluding heart rate change, heart rate variability, EEG, data amongother possible responses induced by the seizure. The sensors 230 mayalso be configured to monitor stimulation levels of the electrode(s) 38(e.g., current, voltage, power, signal frequency, pulse width, signal ONtime, signal OFF time, etc.). The sensors 230 may additionally oralternatively be configured to acquire patient data indicative of one ormore physiological characteristics of the patient prior to the seizure.The one or more physiological characteristics acquired by the sensors230 may include resting heart rate, nocturnal heart rate, heart ratevariability (HRV), among other possible measureable physiologicalcharacteristics.

As shown in FIG. 5, the processing circuit 252 includes a processor 254and a memory 256. The processor 254 may be implemented as ageneral-purpose processor, an application specific integrated circuit(ASIC), one or more field programmable gate arrays (FPGAs), a digitalsignal processor (DSP), a group of processing components, or othersuitable electronic processing components. The memory 256 (e.g., RAM,ROM, Flash Memory, hard disk storage, etc.) may store data and/orcomputer code for facilitating the various processes described herein.Thus, the memory 256 may be communicably connected to the processor 254and provide computer code or instructions to the processor 254 forexecuting the processes described in regard to the VNS analysis system200 herein. Moreover, the memory 256 may be or include tangible,non-transient volatile memory or non-volatile memory. In someembodiments, the memory 256 may include database components, object codecomponents, script components, or any other type of informationstructure for supporting the various activities and informationstructures described herein.

The memory 256 may include various circuits for completing processesdescribed herein. More particularly, the memory 256 includes circuitsconfigured to control operation of the therapy analysis system 200 toassess an efficacy of reducing severity of seizures using VNS therapy.While various circuits with particular functionality may be used, itwill be understood that the memory 256 may include any number ofcircuits for completing the functions described herein. For example, theactivities of multiple circuits may be combined as a single circuit andadditional circuits with additional functionality may be included. Insome embodiments, the circuits of the memory 256 are integrated and/orcombined. Further, it will be understood that the processing circuit 252of the therapy analysis device 200 may further control other processesbeyond the scope of the present disclosure.

As shown in FIG. 5, the therapy analysis system 200 includes acharacteristics database 258, a stimulation manager 260, a stimulationanalyzer 262, and an efficacy/severity analyzer 264. The characteristicsdatabase 258 may be configured to receive and store patient dataindicative of various physiological characteristics of a patient beingassessed for the efficacy of vagus nerve stimulation therapy. Thephysiological characteristics may include a heart rate, an instantaneousheart rate, EEG and/or ECG data, resting heart rate, among otherpossible physiological characteristics of the patient. In someembodiments, at least a portion of the physiological characteristics arereceived by the characteristics database 258 from the sensors 230 (e.g.,resting heart rate, nocturnal heart rate, instantaneous heart rate,etc.). In some embodiments, at least a portion of the physiologicalcharacteristics are received by the characteristics database 258 from aremote server or device collecting EEG signals (e.g., downloadedtherefrom, etc.). As shown in FIG. 5, the characteristics database 258is communicably coupled to the efficacy/severity analyzer 264. Thus, thecharacteristics database 258 may provide the physiologicalcharacteristics to the efficacy/severity analyzer 264 to perform furtheranalysis.

The efficacy/severity analyzer 264 may be configured to receive andstore one or more response features (e.g., change in heart ratepercentage, tachycardia duration, etc.) regarding a efficacy of VNStherapy in reducing seizure severity. The one or more response featuresmay be predefined within the efficacy/severity analyzer 264 and/ormanually input by an operator of the VNS analysis system 200. Theresponse features may be compared to thresholds, or other responsefeatures associated with the patient. The thresholds may be a magnitudedifference and/or a percentage difference (e.g., of a post-treatmentvalue relative to a pre-treatment value, etc.). The efficacy/severityanalyzer 264 may thereby be configured to receive the informationregarding the physiological response of a respective patient to theprovided therapy (e.g., from the stimulation analyzer 262, etc.). Theefficacy/severity analyzer 264 may further determine the patient'ssuitability for continuing VNS therapy based on the one or more responsefeatures and the one or more physiological responses of the patientbeing assessed for VNS therapy efficacy. For example, if the change inheart rate of a patient changes more than the change in heart ratechange threshold (e.g., in response to the stimulation, etc.), the vagusnerve stimulation therapy may be effective at reducing the severity ofseizures for the patient.

In some embodiments, the efficacy/severity analyzer 264 is configured todetermine a synchronizability index for electrodes coupled to the VNSanalysis system. According to an exemplary embodiment, theefficacy/severity analyzer 264 is configured to determine a VNS therapyis effective in reducing the severity of seizure when less electrodesare determined to contribute to the synchronization due to a seizure. Insome embodiments, the efficacy/severity analyzer 264 is configured tocompare a number of electrodes that contribute to synchronization due toseizure before VNS therapy to a number of electrodes that contribute tosynchronization due to seizure after VNS therapy.

The efficacy/severity analyzer 264 may be further configured to providean indication of the efficacy of the vagus nerve stimulation therapy viathe user I/O device 210. In one embodiment, the indication includes thenumber of electrodes contributing to synchronization due to seizure. Inother embodiments, the indication includes a percentage, a value, and/oranother metric indicative of the efficacy of the VNS therapy on theseizures of the patient based on a comparison of pre-treatment severityto post-treatment severity. In other embodiments, the indicationincludes a diagram indicating locations of the electrodes thatcontribute to synchronization due to seizure or levels of electricalactivity in the brain due to seizure.

Referring back to FIG. 5, the stimulation manager 260 may be configuredto control operation of the stimulation device 220 (e.g., the electrodes38, etc.) to provide stimulation to the vagus nerve of a patient. Thestimulation manager 260 may be further configured to monitor and/orcontrol stimulation parameters and/or levels provided by the stimulationdevice 220 (e.g., current, voltage, power, signal frequency, pulsewidth, signal ON time, signal OFF time, etc.). In some embodiments, thestimulation manager 260 is configured to control the stimulation device220 according to a predefined stimulation protocol. In otherembodiments, the stimulation manager 260 is configured to control thestimulation device 220 based on manually input control parametersprovided by an operator of the therapy analysis system 200.

The stimulation analyzer 262 may be configured to receive response dataacquired by the sensors 230 indicative of a physiological response ofthe patient to the stimulation of the vagus nerve. Therefore, thestimulation analyzer 262 may be configured to monitor one or morephysiological responses of the patient to the stimulation includingheart rate changes, HRV changes, among other possible responses inducedby the stimulation. As shown in FIG. 5, the stimulation analyzer 262 iscommunicably coupled to the efficacy/severity analyzer 264. Thus, thestimulation analyzer 262 may provide information regarding thephysiological response of a patient to the efficacy/severity analyzer264 to perform further analysis. In another embodiment, theefficacy/severity analyzer 264 may provide information to thestimulation manager 260 or the stimulation analyzer 262 in order tomodify parameters of the stimulation based on the efficacy of VNStherapy in reducing seizure severity.

FIGS. 6A-6B are flow charts of methods 500 and 600, respectively, ofmeasuring a severity of a seizure and an efficacy of VNS therapy,according to an example embodiment. Method 500 includes determining afirst synchronization, applying a therapy, determining a secondsynchronization and in some embodiments, evaluating an efficacy oftreatment or a severity of seizure. Method 600 includes collecting EEGand ECG data during a seizure, measuring parameters of the data, andusing the measurements to determine an efficacy of the VNS therapy and aseverity of the seizure. Methods 500 and 600 may also include providinglocation information of synchronized electrodes to a physician.

Methods 500 and 600 may be performed one or more times. For example,method 500 may be performed with different therapies applied, such asVNS therapy with different stimulation parameters or a drug therapy withdifferent doses. Method 600 may be performed prior to a patientreceiving VNS therapy and after the patient has received VNS therapy. Insome embodiments, method 600 may be performed multiple times prior to apatient receiving VNS therapy and after the patient has received VNStherapy. In another embodiment, method 600 may be performed multipletimes after the patient has received VNS therapy. The methods 500 and600 may be performed at a given interval (e.g., once a month, every twomonths, etc.).

Referring to method 500, a first synchronization of a first seizure isdetermined at 502. The first synchronization may be determined bycollecting EEG signals from EEG electrodes (e.g., placed on the scalp ofa patient as described with respect to FIGS. 4A and 4B) and analyzingthe EEG signals across all electrodes to determine a spatialsynchronization (e.g., per the method described in FIGS. 9 and 10). Insome embodiments, the first synchronization is determined prior toapplying any therapy. In another embodiment, the first synchronizationis determined after receiving a first therapy.

A therapy is applied at 504. The therapy may be a VNS therapy, a drugtherapy, or a combination thereof. While these are common therapies forepilepsy, other therapies or treatments may be applied as well, such assurgery, a ketogenic diet, or other forms of stimulation. The therapyapplied may be a first therapy with a first set of parameters (e.g.,stimulation parameters, dosage parameters, etc.). In another embodiment,the therapy applied may be a therapy other than the first therapy. Ifthe therapy applied is not the first therapy, a second set of parametersmay be used that are different from the first set of parameters. In someembodiments, the first and second therapies may not be the same type oftherapy.

A second synchronization of a second seizure is determined at 506. Thesecond synchronization may be determined using the same techniques asdetermining the first synchronization at 502.

In some embodiments, a efficacy of therapy and/or a severity of seizuremay be evaluated at 508. The efficacy of therapy and/or severity ofseizure may be evaluated by comparing the first synchronization to thesecond synchronization. In some embodiments, the efficacy of therapyand/or severity of seizure may be determined by comparing the firstsynchronization, the second synchronization, or both the first andsecond synchronization to a threshold. The severity of seizure may beevaluated separately based on the first and second synchronization, oras a change in severity based on both the first and secondsynchronization. Additional methods for evaluating the efficacy oftherapy and/or a severity of seizure are described with respect to FIG.6B.

Now referring to method 600, as discussed above, the vagus nervestimulator 10 may be equipped with a heart rate sensor to collect ECGsignals at 602. In some embodiments, electrodes may be placed on thepatient's limbs or chest to collect ECG signals. Epileptic seizures canlead to changes in autonomic function affecting the sympathetic,parasympathetic, and enteric nervous systems. Changes in cardiac signalsare potential biomarkers that may provide an extra-cerebral indicator ofictal onset in some patients.

Heart rate can be measured easily when compared to other cardiacbiomarkers (e.g., ST segment elevation, T-wave alternans), and thereforemay be used to assess the effect of seizures on cardiovascular function.Understanding the magnitude and duration of heart rate changesassociated with seizures may be used to determine the effect of atherapy on cardiovascular dynamics during seizures. However, ECG signalsare typically affected by muscle/motion artifacts and therefore, itbecomes difficult to estimate actual changes in heart rate in anautomated manner. Analysis of the ECG data will be discussed more withrespect to FIG. 7.

EEG data is collected using the EEG electrodes or sensors placed on thescalp of a patient (e.g., as described with respect to FIGS. 4A and 4B)at 602. When measuring on the scalp of the patient, electrical activityof neurons in the brain is diffused through layers of tissue and theskull before reaching an electrode. This causes electrical activity toshow up on multiple channels or electrodes, allowing the electricalfields to be mapped. However, this also causes dilution of the specificlocation of the neural activity, such as the neural activity seen duringa seizure. To compensate for this phenomena, the electrode channels aredecorrelated to determine independent sources of activity. This will bedescribed below with respect to FIGS. 8-11.

The ECG data and EEG data are used to determine change in heart rate inpercentage at 604, tachycardia duration at 606, and determine a measureof spatial synchronization of electrodes at 608. Each of these methodswill be described in detail herein according to exemplary embodiments.

The measurements can be used to determine a severity of the seizure at610 and/or an efficacy of the VNS therapy at 612. If a single set ofmeasurements are determined (i.e., method 600 occurred once), theseverity of the seizure may be determined using the measurements. Thiswill be described in further detail herein. If method 600 is performedmultiple times, the severity of each seizure and the efficacy of the VNStherapy may be determined. The efficacy and/or severity of the VNStherapy may be determined by comparing two or more sets of measurements.In another embodiment, the efficacy and/or severity may be determined bycomparing the sets of measurements to a plurality of thresholds. Forexample, a single threshold may be used, wherein a comparison of themeasurements to the threshold indicates “severe” or “not severe” forseverity of seizure, and “effective” or “not effective” for efficacy oftherapy. In another embodiment, multiple thresholds may be implemented.For example, when determining severity, a comparison to a firstthreshold and second threshold may indicate “severe,” “moderatelysevere” and “not severe.” For example, when determining efficacy, whenthe difference between the measured synchronization before and aftertreatment is above a first value, “very efficacious” or an indicatorindicating a higher level of efficacy may be indicated, when thedifference is above a second value, “somewhat efficacious” or anindicator indicating a moderate level of efficacy may be indicated andwhen the difference is below the second value, “not efficacious” or anindicator indicating a low level of efficacy may be indicated. However,more or less thresholds may be used to indicate varying levels ofseverity and/or efficacy. The thresholds may be user specific, or may bebased on data from a population of patients. The efficacy of VNS therapymay be determined by performing statistical tests (e.g., t-test, f-test,etc.) on the measured parameters. The efficacy and/or severity may bedetermined by a physician observing the measured parameters. In someembodiments, the efficacy and/or severity is determined by a combinationof the aforementioned methods.

Method 600 may also include providing seizure information to aphysician. The information provided to the physician may include theseverity of the seizures, the efficacy of the VNS therapy, the locationof the synchronized channels, etc. The information provided to thephysician will be described in more detail with respect to FIGS. 9-12.

Now referring to FIG. 7, a flow chart of a method 700 of analyzing ECGdata according to FIG. 6 is shown, according to an example embodiment.The ECG data is analyzed to determine the heart rate change, inpercentage, during a seizure, and the duration of increased heart rateduring a seizure.

A raw ECG signal is collected using the ECG electrodes or the heart ratesensor as discussed above at 702. The raw ECG signal can be seen in FIG.8A.

A despiking algorithm is applied to the ECG signal in order to removeartifacts from the heart rate (HR) signal. This may be achieved by firstextracting an RR-interval, a time interval between the peaks of twoadjacent R waves from the ECG signal, and calculating an instaneousheart rate (IHR) (i.e., IHR=60/(RR-interval)) at 704.

A phase space despiking is applied to the instantaneous heart rate at706. FIG. 8B shows the instantaneous heart rate (IHR) verses timecorresponding to the ECG signal of FIG. 8A, before and after despiking.The phase space despiking approach includes constructing an ellipsoid inthree-dimensional phase space, and identifying spikes as points that areoutside of the ellipsoid. The method iterates until a number of newpoints identified as spikes fall to zero. This approach is illustratedin FIG. 8C and the steps per iteration are outlined below:

1) From the original time series u_(i) (e.g., HR) a first and secondderivative is approximated as

$\begin{matrix}{{\nabla u_{i}} = {\left( {u_{i + 1} - u_{i - 1}} \right)/2}} & (1) \\{{\nabla^{2}u_{i}} = \frac{{\nabla u_{i + 1}} - {\nabla u_{i - 1}}}{2}} & (2)\end{matrix}$

2) The standard deviations of all three variables, σ_(u), σ_(∇u), andσ_(∇) ₂ _(u) is calculated, and the expected maxima is calculated usingthe Universal criterion, μ_(u){circumflex over (σ)}=√{square root over(2 ln n)}{circumflex over (σ)}, where n is the number of time samples inu, μ_(u)√{square root over (2 ln n)} is the Universal threshold, and{circumflex over (σ)} is the estimated standard deviation.

3) The rotation angle of the principal axis of ∇²u_(i) versus u_(i) iscalculated using the cross correlation:θ=tan⁻¹(Σu_(i) ∇² u _(i)/Σu_(i) ²)   (3)

(Note: for ∇u_(i) versus u_(i) and for ∇²u_(i) versus ∇u_(i)θ≈0 becauseof symmetry.)

4) For each pair of variables, the ellipse that has maxima and minimafrom u_(i), ∇u_(i), and ∇²u_(i) is calculated. Thus, for ∇u_(i) versusu_(i),the major axis is μ_(u)σ_(u) and the minor axis is μ_(u)σ_(∇u);for ∇²u_(i) versus ∇u_(i) the major axis is μ_(u)σ_(∇u) and the minoraxis is μ_(u)σ_(∇) ₂ _(u); and for ∇²u_(i) versus u_(i); the major andminor axes, a and b, respectively, can be shown to be the solution of(μ_(u)σ_(u))² =a ² cos² θ+b ² sin² θ  (4)(μ_(u)σ_(∇) ₂ _(u))² =a ² sin² θ+b ² cos² θ  (5)

5) For each projection in phase space, the points that lie outside ofthe ellipse are identified and replaced using the cubic polynomialinterpolation.

At each iteration, replacement of the spikes reduces the standarddeviations calculated in step 2 and thus the size of the ellipsoidreduces until further spike replacement has no effect. FIG. 8A shows anexample of the recorded ECG from a left temporal lobe seizure. Verticalline 800 (at time 0sec) indicates a start time of the seizure. Trace 802in FIG. 8B shows the corresponding instantaneous heart rate (IHR). FIG.8B shows the spikes in the IHR (marked with circles (e.g., circle 804))that could be due to the erroneous detection of noise/artifacts in theECG signal. Following the application of the despiking algorithm, atrace 806 in FIG. 8B shows the despiked IHR. The despiked HR is thensmoothed using the pseudo-Gaussian smoothing (e.g., with a window ofsize 5), as seen as trace 808 in FIG. 8B, prior to estimation of HRchange percentage and duration associated with the seizure.

Using the smooth despiked HR, HR_(Max), HR_(pre) and HR_(post) aredetermined at 708. The maximum of IHR change (HR_(Max)) is found usingthe despiked IHR in a time window (defined as seizure window 810hereafter) (e.g., 450 sec), extending from a time period (e.g., 150 sec)before a seizure annotation (time 0 in FIG. 8D) to a time period (e.g.,300 sec) after the annotation. In addition, the mean of IHR in apre-ictal window 812 and a post-ictal 814 window is calculated, namelyHR_(pre) and HR_(post) respectively. The pre-ictal window 812 mayimmediately precede the seizure window 810 and may last a predeterminedamount of time (e.g., 100 sec). In addition, the post-ictal window 814may immediately follow the seizure window 810 and may last apredetermined amount of time (e.g., 100 sec). While the pre-ictal window812 and the post-ictal window 814 are shown to immediately precede andfollow the seizure window 810, there may be a time delay between thepre-ictal and post-ictal windows and the seizure window. In someembodiments, the pre-ictal and post-ictal windows are the same amount oftime, as shown in FIG. 8D. In another embodiment, the pre-ictal andpost-ictal windows span different amounts of time. The HR changepercentage is calculated as HR change %=(HR_(max)−HR_(pre))/HR_(pre)×100at 710. In addition, a tachycardia duration (TCD) is calculated at 712.TCD is estimated as a difference between the time of a first IHR valuethat is lower than HR_(pre) in the values preceding HR_(Max) and a firstIHR value that is lower than HR_(post) in the values following HR_(Max).The HR change % and TCD are estimated per seizure and used as twocardiac-based features for quantifying a severity of one or moreseizures. While the change in heart rate percentage and the tachycardiaduration are used as the two cardiac-based features for quantifyingseizure severity, other cardiac parameters could be used in addition, orin place of these features. In some embodiments, only one cardiac-basedfeature is used to quantify seizure severity. In other embodiments, morethan two cardiac-based features may be used to quantify seizureseverity. Further, it should be appreciated that the process describedabove with respect to FIG. 7 is only one example method for processingthe ECG data; other methods of processing the ECG data may be utilizedin other embodiments and fall within the scope of the presentdisclosure.

In addition to affecting heart rate, epileptic seizures aremanifestations of intermittent spatiotemporal transitions of the humanbrain from the interitcal state to the ictal state. Dynamical analysisof EEGs at multiple sites of the epileptic brain has shown a progressivechange in synchronization prior to and during epileptic seizures. Thus,one could postulate that if a therapy is effective, it would alter thesynchronization of brain dynamics. One issue that affects themeasurement of synchronization using scalp EEGs is that mostclosely-spaced EEG electrodes appear to be always correlated due to thecommon-source of activity that they receive from deep brain sources thatare immediately underneath or spatially distant. These spatially distantsources tend to artificially increase the value of synchronization.Methods of independent component analysis and principal componentanalysis may be used to address this issue; however, these methodstransform the electrode-space into a new set of basis functions that maynot have any clinical relevance. Due to this reason, it becomesdifficult to clinically interpret the synchronization values provided byapplying bivariate (pair-wise) approaches of measuring brain synchronysuch as cross-correlation, phase synchronization on the transformedsignal space. To resolve this issue, synchronization in the electrodespace can be estimated by measuring the contribution of each electrodeto the global network synchronization, a quantity defined as asynchronizability of the electrode.

FIG. 9 is a flow chart of a method 900 of determining asynchronizability of each EEG electrode and a total number ofsynchronized EEG electrodes, according to an exemplary embodiment.Method 900 includes a plurality of equations for determining asynchronizabiltiy of each electrode. However, these calculations andequations are not meant to be limiting and are provided only by way ofexample. Other equations or calculations that identify or estimate acontribution of individual electrodes to a synchronization may be usedwithout departing from the scope of the present disclosure. FIG. 10 is avisual illustration of various operations of the method 900 shown inimages to illustrate how a physician may see or interpret the data,according to an example embodiment.

EEG signals are collected for a patient during a seizure at 902. Theelectrodes may be arranged on a head of the patient similar to theconfiguration shown in FIGS. 4A and 4B. However, other configurationsmay be used and are intended to fall within the scope of thisdisclosure. The EEG signal may be segmented into non-overlapping timeperiods or epochs (e.g., 2 seconds).

For each electrode, the EEG signal is filtered and artifacts are removedat 904. A bandpass filter may be used (e.g., between 0.5 and 30 Hz). Insome embodiments, a high pass filter is used. In another embodiment, alow pass filter is used. In yet another embodiment, a combination offilters may be used to filter the EEG signal. EEG high amplitudeartifacts may be removed using an artifact-blocking (AB) algorithm. Thisis a technique that enables artifact removal without eliminating anyepoch of the signal. Since the EEG is non-stationary in general, it maybe appropriate to use the time-frequency domain methods like an discretewavelet transform (DWT) analysis to describe EEG in the time andfrequency domain (e.g., Daubechies-4 wavelet for the analysis ofepileptic EEG). However, other methods of artifact removal may be used.

Wavelet decomposition of each epoch is performed for each electrode at906. In some embodiments, to decompose the wavelets, the EEG data isdecomposed into four frequency sub-bands of δ(e.g., 0 Hz-4 Hz), θ(e.g.,4 Hz-7 Hz), α (e.g., 8 HZ-15 HZ), and β(e.g., 16 Hz-30 Hz). However, fewor less frequency sub-bands may be used in other embodiments. Forexample, a γ(>30 Hz) frequency may be included. In some embodiments,frequency sub-bands may be combined into larger frequency sub-bands(e.g., 0 Hz-7 Hz and 8 Hz to 30 Hz). In another embodiment, thesub-bands may be broken down into narrower frequency ranges. In someembodiments, the sub-bands may have different ranges (e.g., α is 7 Hz-14Hz).

Maximum wavelet coefficients (MaxMC) are extracted at 908. The MaxWC ineach sub-band for each epoch is used as a feature to represent thetime-frequency distribution of EEG signals for that epoch. This resultsin a time profile of MaxWC values per EEG electrode. FIG. 11A shows theraw EEG signal across multiple electrodes centered around a seizure andthe corresponding MaxWC in δ frequency sub-band is shown in FIG. 11B.From these figures, it can be seen that MaxWC attains higher valuesduring a seizure than baseline activity. Furthermore, the values ofMaxWC of FIG. 11B are higher in the left hemisphere which is consistentwith the seizure focus (left temporal lobe) in this example embodiment.While the MaxWC is used, other features of the decomposed wavelets maybe used (e.g., average).

A spatial synchronization across all electrodes is calculated at 910.The synchronizability of each EEG electrode is defined as thecontribution that each electrode makes to the global networksynchronization. The global spatial synchronization across allelectrodes per frequency sub-band is calculated by estimating the ratioof maximum to minimum singular value of the wavelet coefficients xelectrodes matrix. Thus, for each sub-band d and epoch k, the globalspatial synchronization {tilde over (θ)}_(k) ^(d) may be defined as

$\begin{matrix}{{\overset{\sim}{\theta}}_{k}^{d} = {{\sigma_{r}^{d}(k)}/{\max_{k}\left( {\sigma_{r}^{d}(k)} \right)}}} & (6) \\{{\sigma_{r}^{d}(k)} = \frac{\max\left( {\sigma^{d}(k)} \right)}{\min\left( {\sigma^{d}(k)} \right)}} & (7)\end{matrix}$

where σ^(d)(k) is the singular values vector of wavelet coefficients ofthe k^(th) epoch in sub-band d and σ_(r) ^(d)(k) is the ratio of themaximum to the minimum of the singular values. Since {tilde over(θ)}_(k) ^(d) is a normalized form of σ_(r) ^(d)(k), therefore {tildeover (θ)}_(k) ^(d) ∈ [0,1]. When a seizure occurs, the synchronizationamong electrodes increases and results in a higher value of {tilde over(θ)}_(k) ^(d). The global synchronization {tilde over (θ)}_(k) ^(d) isused to weigh the wavelet power contribution of each electrode. In orderto accomplish this, a relative index of MaxWC per electrode may becreated. Thus, in some embodiments, for each value of MaxWC^(d)(k, j)for epoch k, electrode j and sub-band d (d=δ, θ, α, or β) the relativeindex is defined as

$\begin{matrix}{{\overset{\sim}{g}}_{k,j}^{d} = \frac{{Max}\;{{WC}^{d}\left( {k,j} \right)}}{{Max}\;{{WC}_{ref}^{d}(j)}}} & (8)\end{matrix}$

where MaxWC_(ref) ^(d)(j) is the moving average of MaxWC points in thewindow [k−5,k−20] of electrode j. Since, similar values of {tilde over(g)}_(k,j) ^(d) across different patients or even different electrodesof the same patient may represent different brain states (i.e. seizurevs. non-seizure), the value of {tilde over (g)}_(k,j) ^(d) may be scaledusing the following nonlinear scaling function g: R²→[0,1]

$\begin{matrix}{g_{k,j}^{d} = \left\{ \begin{matrix}{0,} & {{{if}\mspace{14mu}{\overset{\sim}{g}}_{k,j}^{d}} < 0.05} \\{\frac{{\overset{\sim}{g}}_{k,j}^{d}}{10},} & {{{if}\mspace{14mu} 0.05} < {\overset{\sim}{g}}_{k,j}^{d} < 10} \\{1,} & {{{if}\mspace{14mu}{\overset{\sim}{g}}_{k,j}^{d}} > 10}\end{matrix} \right.} & (9)\end{matrix}$

Using the spatial synchronization across all electrodes per frequency, asynchronizability index (SI) is estimated at 912. According to someembodiments, the synchronizability index is estimated per electrode jand epoch k for sub-band d asSI_(k,j) ^(d)=(g _(k,j) ^(d))^(θ) ^(k) ^(d)   (10)

where θ_(k) ^(d)=Ω−({tilde over (θ)}_(k) ^(d)−{tilde over (θ)}_(ref)^(d)), {tilde over (θ)}_(ref) ^(d) is the mean of {tilde over (θ)}_(k)^(d) in the window [k−5, k−20]. Since g_(g,j) ^(d) ranges from 0 to 1and ({tilde over (θ)}_(k) ^(d)−{tilde over (θ)}_(ref) ^(d)) is between−1 and 1, Ω=1.5 is empirically selected to ensure that SI ∈[0,1].However, other values of Ω may be selected. FIG. 11C shows the SI valuesfor all electrodes in δ frequency sub-bands for the MaxWC valuesdepicted in FIG. 11B. FIG. 11C shows that SI values of multipleelectrodes increase significantly at seizure onset (time 0).

The SI per electrode may be used to determine a significance of theelectrode and the number of significant electrodes at 914. The number ofelectrodes that achieve statistically significant increase in SI duringseizures may be a feature used to study the effects of VNS on EEGspatial synchronization. According to some implementations, this may beachieved by applying a one-sided robust statistical CUSUM procedure inthe following recursive manner: Step 1. Calculate μ_(ref) ^(d)(k,j) andσ_(ref) ^(d)(k,j) as the mean value and standard deviation of the SIcalculated for the window [k−5, k−20] and j^(th) electrode for sub-bandd; Step. 2. Determine an adaptive threshold γ_(k,j) ^(d) asγ_(k,j) ^(d)=μ_(ref) ^(d)(k,j)+3σ_(ref) ^(d)(k,j)   (11)h _(k,j) ^(d)=αΣ_(ρ=k−M+1) ^(k)γ_(ρ,j) ^(d)   (12)

where 0≤α≤1; Step. 3 Estimate the CUSUM statistic recursively as:U _(k,j) ^(d)=max{0, (SI_(k,j) ^(d)−γ_(k,j) ^(d))+U _(k−1,j) ^(d)}  (13)

Step 4. Identify the j^(th) electrode for epoch k in sub-band d withstatistically significant value of SI as:

$\begin{matrix}{\psi_{k,j}^{d} = \left\{ \begin{matrix}{1,} & {{{if}\mspace{14mu} U_{k,j}^{d}} \geq h_{k,j}^{d}} \\{0,} & {otherwise}\end{matrix} \right.} & (14)\end{matrix}$

The number of electrodes that attain a significant value of SI acrossany of the sub-bands during the seizure may be used as the EEG-basedfeature to study the effects of closed-loop VNS, including the efficacy.If the electrodes are determined to contribute to the synchronizationdue to the seizure, they may be indicated as such for the physician at916. If the electrodes are determined to not contribute to thesynchronization due to the seizure, they may be indicated as such forthe physician at 918.

The EEG-based data may be provided to the patient and/or physician in avariety of ways. For example, the electrodes may be overlaid on an MRIimage of the patient with synchronized electrodes being displayed in onemanner, while electrodes that do not contribute to the synchronizationare displayed in a different manner. In some embodiments, the electrodesmay by overlaid on a standard image of a head or brain instead of animage unique to the patient. In both cases, the electrodes may appear asdifferent colors (e.g., red for synchronized and yellow for notsynchronized) on a display for the physician. In another embodiment,different symbols may be used to represent synchronized and notsynchronized electrode (e.g., solid circle for synchronized and opencircle for not synchronized). In some embodiments, the code of theelectrode, as labeled in FIGS. 4A and 4B, may be displayed to thephysician in the form of a table, list, or chart differentiating betweenelectrodes that contribute in synchronization and electrodes that do notcontribute in synchronization. The shape and color selection is used asan example and is not intended to be limiting, and combination ofcolors, symbols, numbers, etc. may be used to differentiate betweenelectrodes that contribute in synchronization and electrodes that do notcontribute in synchronization. In other embodiments, the graphical dataas seen in FIGS. 11A-11C may be provided. In some embodiments, graphicalindications (e.g., text, colors, symbols, etc.) may be provided thatprovide a quantitative indication of synchronization for individualelectrodes and/or for sets of electrodes based on the measurementsdetermined for each electrode. In some embodiments, quantitativeindication of synchronization may be shown for a comparison of a firstseizure to a second seizure. For example, blue may be used to indicateno synchronization for a given electrode for both the first and secondseizure, green may indicate a change from synchronization to nosynchronization for a given electrode from the first seizure to thesecond seizure, yellow may indicate a synchronization for a givenelectrode for both the first and second seizure and red may indicate achange from no synchronization to synchronization for a given electrodefrom the first seizure to the second seizure. While four quantitativeindications are described, any number of indications may be used. Inaddition, different shapes or symbols may be used instead of colors todifferentiate the indications. In another embodiment, the quantitativeindication of synchronization may be shown for a single seizure. Forexample, blue may be used to indicate no synchronization for a givenelectrode, green may indicate a low synchronization for a givenelectrode, yellow may indicate a moderate synchronization for a givenelectrode and red may indicate a high synchronization for a givenelectrode. While four quantitative indications are described, any numberof indications may be used. Alternatively, instead of using distinctcolors, a spectrum can be used that ranges from no synchronization tohigh synchronization. In addition, different shapes or symbols may beused instead of colors to differentiate the indications.

FIGS. 12A, 12C, 12E, and 12G are images of a wavelet coefficient changeas a result of seizure before VNS therapy and FIGS. 12B, 12D, 12F and12H are images of a wavelet coefficient change as a result of seizureafter VNS therapy, according to an example embodiment. One patients isshown in each grouping (FIGS. 12A and 12B, FIGS. 12C and 12D, FIGS. 12Eand 12F, and FIGS. 12G and 12H). The method followed to obtain theseimages is substantially similar to method 600 of FIG. 6, which mayinclude method 900.

For each patient, the electrodes that attain a significant value ofsynchronizability index (SI) for most of seizures are defined as theones that contributed the most to the ictal spatial synchronization. Themaximum of wavelet coefficients (MaxWC) was calculated in a 450 sec timewindow (defined as seizure window), extending from 150 sec before theseizure annotation to 300 sec after the annotation and the mean value ofthe wavelet coefficients in a 100 sec preictal window that isimmediately preceding the seizure window namely, WC_(pre) for each ofthese electrodes. The change in each EEG electrode activity wascalculated as WC_(change)=MaxWC/WC_(pre). FIGS. 12A-12H demonstrate aWC_(change) that is projected on scalp both prior and after VNS therapyusing the standard 10-20 EEG system with 19 electrodes. WC_(change) wascalculated for the four frontal electrodes (F7, F3, F4, F8) along withfive left electrodes (T3, T5, O1, C3, P3) and five right electrodes (T4,T6, O2, C4, P4). The anatomical location of these electrodes can be seenin FIGS. 4A-4B.

FIG. 13 is a table showing a summary of patients' characteristics for astudy conducted relating to the methods described above. 51 patients(female (n=31, 60.78%), male (n=20, 39.22%), mean age of 37.69 (ranges18-69), with the standard deviation of 13.67) were enrolled across twoclinical trials (NCT01325623, NCT 01846741) wherein patients wereimplanted with an AspireSR® VNS Therapy System. Each subjectparticipated in this study for a minimum of 5 weeks to include at least1 week pre-implant, 2 weeks of post-implant recovery, one week ofstimulation titration, and approximately 3 to 5 days of epilepsymonitoring unit (EMU) evaluation. At the beginning of the EMU visit,output current was increased to target level (at least 0.5 mA) that wastolerable for the patient and then the patient was set to the automatedmagnet mode (AMM) stimulation, which automatically delivers stimulationwhen a seizure associated with ictal tachycardia is detected. ConcurrentEEG and ECG data was collected from all patients during the EMUevaluation. EEG data was recorded according to standard 10-20 systemusing 19 electrodes (in positions Fp1, Fp2, F7, F3, Fz, F4, F8, T7, C3,Cz, C4, T8, P7, P3, Pz, P4, P8, O1, O2) and the ECG are recorded viasurface ECG electrodes. However, more or less electrodes could have beenused. The sampling frequency was different from patient to patient withthe range of 256 Hz to 2000 Hz.

In addition, for each patient, EEG and ECG data from a previous EMUvisit that was prior to VNS implantation was also collected. Theseseizures will be referred to as “pre-treatment” seizures while theseizures that occurred in these patients following initiation ofAMM-based VNS implantation will be called “post-treatment” seizures. 13out of 51 patients were removed from this analysis because they eitherdid not have EEG or ECG data for either the pre-treatment orpost-treatment phase or had concurrent medication changes during thepost-treatment phase and therefore may confound the evaluation of thetrue effect of the AMM-based VNS therapy.

Seizure annotations were provided by the clinical study sites followinginvestigator review of both electrographic and clinical (video) data.Investigators were instructed to document the earliest seizure onsettime indicated by the combination of the video and EEG input data. Thenumber of annotated seizures in each patient both pre- andpost-treatment are provided in FIG. 13. From FIG. 13, a total of 124pre-treatment and 156 post-treatment seizures were analyzed in thestudy.

FIG. 14A is a graph displaying a magnitude and duration of a heart rateincrease during a seizure before VNS therapy while FIG. 14B is a graphdisplaying a magnitude and duration of a heart rate increase during aseizure after VNS therapy. To evaluate the efficacy of the closed-loopVNS therapy in reducing ictal spatial synchronization (as measured byEEG-based feature) and the cardiac effects of seizures (as measured byECG based features), the ECG- and EEG-feature extraction methodsdescribed above for determining 1) heart rate change ((in %), 2)duration of the heart rate change (in seconds), and 3) measure ofspatial synchronization of EEG during a seizure were applied to therecorded EEG and ECG dataset from 38 patients (see FIG. 13). Seizuresthat occurred during automatic stimulation were compared to seizuresthat occurred prior to VNS therapy to evaluate if severity was reduced.FIGS. 14A and 14B present a representative example of the IHR versustime for Patient X pre- and post-treatment. FIG. 14A shows a HR changeduring a pre-treatment seizure to be 52.96% with the tachycardiaduration of 163.57 sec. For the same patient, the value of HR change andtachycardia duration following VNS therapy is decreased to 8.57% and82.31 sec respectively (see FIG. 14B), a substantial reduction of 44.39%in heart rate increase and 81.22 sec in tachycardia duration.

To demonstrate the effect of VNS therapy on EEG signals, FIGS. 15A and15B shows the SI values for electrodes in four frequency sub-bands of δ,θ, α and β for a pre-treatment (FIG. 15A) and post-treatment (FIG. 15B)seizure. From FIG. 15, it can be seen that the pre-treatment SI valuesare higher around seizure (time zero) in comparison to the pre-ictal SIvalues irrespective of the frequency band, suggesting an increase in thesynchronizability of electrodes during seizure. Following treatment viaclosed-loop VNS therapy, the SI values decrease significantly for allelectrodes suggesting a substantial reduction in synchronizability ofthe multiple EEG electrodes. This suggests that VNS therapy couldeffectively reduce the severity of brain activity caused by seizure inthis patient. The overall performance of VNS therapy across all 38patients using the three selected features of seizure severity isdescribed below.

FIG. 16A is the synchronizability index for the δ frequency sub bandsshowing the synchronized electrodes during a seizure before VNS therapywhile FIG. 16B is the synchronizability index for the δ frequency subbands showing the synchronized electrodes during a seizure after VNStherapy, for a given patient. The synchronized electrodes during aseizure are designated by a filled in circle (“●”), while the electrodesthat are determined to not be synchronized are designated with an opencircle (“∘”). When comparing the number and location of the synchronizedelectrodes of FIG. 16A (“pre-treatment”) to the number and location ofthe synchronized electrodes of FIG. 16B (“post-treatment”), it can beseen that the number of synchronized electrodes decreased. However,FIGS. 16A and 16B show examples of the number and location ofsynchronized electrodes. The number and location of the synchronizedelectrodes may vary patient to patient and may vary seizure to seizurefor a given patient. In addition, the number and location of thesynchronized electrodes may vary based on the severity of the seizure,or the type of seizure.

FIG. 17 is a three-dimensional view of a seizure severity feature space,according to an example embodiment. A total of 280 seizures (124pre-treatment and 156 post-treatment) were available for analysis (SeeFIG. 13). The three selected features per seizure and across allseizures was provided to a unsupervised Fuzzy-C-Mean classifier forclustering. This classifier divides these features into two clustersbased on their hidden natural patterns and without considering theirlabels (pre- or post-treatment). Thus, the seizures in the same clusterare as similar as possible, while the seizures in different clusters areas dissimilar as possible. More specifically, the set of candidate ECGand EEG derived features namely 1) tachycardia HR change %, 2)tachycardia duration (sec), and 3) EEG electrodes that attain a highsynchronizability index during a seizure, were estimated per seizure andacross all seizures. All features were then normalized using theircorresponding z-score value prior to input into the classifier.

Since the goal of the classification step in this analysis was todetermine whether the selected features have significant discriminativepower to correctly label seizures as being either from the group ofpre-treatment or post-treatment, an unsupervised classifier wasutilized. An advantage of unsupervised classifiers is that they do notutilize any training data for classification. Rather, this family ofclassifiers aggregates features into different classes based on thenatural clusters that may exist in the feature values. Thus, if theselected features separate the seizures into two distinct classes ofpre-treatment and post-treatment seizures, then it implies that theselected features have the ability to quantify the effectiveness of VNStherapy. In this study, the unsupervised Fuzzy-C-Mean (FCM) algorithmwas used to implement the classifier. This algorithm is an iterativeclassification method that gives comparatively better than k-meansalgorithm for overlapped data set and can separate the two classes usingleast number of clusters. Results from the application of this procedureon the data are presented next. FIG. 17 depicts a three-dimensionalfeature space with the pre- and post-treatment seizures marked withcircle and star markers respectively. This figure shows the ability ofthe classifier to cluster the seizures into two classes using the 3selected features.

FIG. 18A is a table showing the statistical data as a result of thestudy performed. The mean value and standard deviation of the selectedfeatures of seizure severity across all seizures from the 38 patientsbefore and after application of VNS therapy is shown, along with theircorresponding t- and p-values. The t-statistic values in FIG. 18A wereproduced using a Welch t-test (which is the conventional student t-testgeneralized for unequal sample sizes and variances between the groups).From FIG. 18A, it appears that the mean value across all seizures andpatients for each of the selected discriminating features issignificantly lower (p<0.05) for post-VNS treatment compared to pre-VNStreatment. Overall, this suggests that each selected featureindependently has the ability to statistically show the effect of VNStherapy on EEG synchronization and cardiac effects related to seizures.If there are significant correlations between selected features ofseverity, then the joint behavior of the features can be morediscriminating than each feature taken individually. Therefore, the FCMclassifier can be applied to evaluate the performance of VNS therapyusing all there features in combination, as described herein.

FIG. 18B is a table showing the classification performance in predictingseizures according to the results of the study. Furthermore, FIG. 18Bdemonstrates that the classifier is capable of clustering pre- andpost-treatment seizures with an accuracy of 83.57%. This indicates thatthe combination of selected features show the effect of VNS therapy inreducing ictal EEG synchronization and the magnitude and duration ofheart rate increase. Furthermore, it appeared that 21 post-treatmentseizures were mis-classified as pre-treatment seizures. From these 21post-treatment seizures, 11/21 seizures occurred in 5 patients (Patients4, 9, 19, 24, 37 in FIG. 13), suggesting that the application of VNStherapy did not have an effect of the seizures of these patients. In theremaining 10/21 misclassified seizures, there was a reduction to a lowervalue in all the 3 selected features, however, the change was notstatistically significant (p>0.05). Also, 25 pre-treatment seizures weremis-classified as post-treatment seizures. Further review of the EEGconfirmed that 18/25 seizures appeared to be psychogenic non-epilepticevents and the remaining 7/25 seizures had higher values in all 3selected features, however, they were not statistically significant(p>0.05).

FIG. 18C is a table showing the classification of seizures that receivedVNS therapy according to the results of the study. Specifically, theability of the classifier to discriminate the seizures that receivedacute responsive VNS therapy (as verified using the AspireSR device logfiles) from the pre-treatment ones was analyzed. Using this definition,70 out of 156 post-treated seizures were considered for thismeta-analysis. The corresponding classification performance is shown inFIG. 18C. The classifier is again capable of discriminating the twoclasses with an accuracy of 79.90%, where the misclassifiedpost-treatment seizures are the same as the ones noted in FIG. 18B. Thisfinding suggests that VNS therapy could also be able to acutely effectthe severity of seizures.

In summary, this study proposed a method to determine the effectivenessof VNS therapy in reducing the severity of the seizures in epilepticpatients from their EEG and ECG signals around the seizure annotation.Two data sets that were collected from 13 European sites and 10 US sitesare used to build a classifier, which determines if the seizure happensbefore or during the VNS therapy. The process consists of the followingtwo components: feature extraction and classification procedure. In thisstudy, a set of 3 “discriminating features”, the heart rate change %,tachycardia duration, and affected electrodes due to the seizure wasidentified that could distinguish the severity of the seizures pre- andpost-treatment in a group of 29 subjects who were under VNS therapy. Theunsupervised FCM classifier was used. The unsupervised approach may bepreferable to the supervised one in areas of complex topography. In suchconditions, selecting the training data set is usually difficult insupervised approach because of the variability of spectral responseswithin each class. Consequently, an appropriate training data collectioncan be very hard and time consuming. Conversely, spectrallydistinguishable classes could be classified by the unsupervised approachaccurately.

The selected discriminating features found to be able to distinguish 1)all pre- treatment seizures from post-treatment ones and 2) the acutepost-treatment seizures with the HR change % of above 20% frompre-treatment ones. In both cases the Fuzzy C-mean classifiers show morethan 80% performance. The findings suggest that combining EEG and ECGsignals with clustering techniques may provide insights into thetherapeutic effect of VNS therapy.

In view of the above, EEG synchronization data may be used to evaluatethe effectiveness of VNS therapy in reducing seizure severity inpatients with epilepsy. To quantify the severity of each seizure, threefeatures may be extracted: (1) heart rate change (in %) during aseizure, (2) duration of the heart rate change (in seconds), and (3)measure of spatial synchronization of EEG during a seizure. Thesefeatures may be used to manage and/or adjust the automated delivery ofVNS Therapy to a patient based on seizure detection, and may further beused to configure therapy to evaluate and reduce seizure severity. Thisanalysis technique and the embodiments utilizing this technique mayprovide an objective quantification of the effects of closed-loopneuromodulation. Furthermore, the evaluation of VNS Therapy usingdynamical analysis of EEG-ECG data may provide insights into themechanism of action of this therapy on seizures as well as on theassociated co-morbidities.

The present disclosure contemplates methods, systems, and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a machine, the machine properly views theconnection as a machine-readable medium. Thus, any such connection isproperly termed a machine-readable medium. Combinations of the above arealso included within the scope of machine-readable media.Machine-executable instructions include, for example, instructions anddata which cause a general purpose computer, special purpose computer,or special purpose processing machines to perform a certain function orgroup of functions.

Although the figures may show a specific order of method steps, theorder of the steps may differ from what is depicted. Also two or moresteps may be performed concurrently or with partial concurrence. Suchvariation will depend on the software and hardware systems chosen and ondesigner choice. All such variations are within the scope of thedisclosure. Likewise, software implementations could be accomplishedwith standard programming techniques with rule based logic and otherlogic to accomplish the various connection steps, processing steps,comparison steps and decision steps.

Also, the term “or” is used in its inclusive sense (and not in itsexclusive sense) so that when used, for example, to connect a list ofelements, the term “or” means one, some, or all of the elements in thelist. Conjunctive language such as the phrase “at least one of X, Y, andZ,” unless specifically stated otherwise, is otherwise understood withthe context as used in general to convey that an item, term, etc. may beeither X, Y, Z, X and Y, X and Z, Y and Z, or X, Y, and Z. Thus, suchconjunctive language is not generally intended to imply that certainembodiments require at least one of X, at least one of Y, and at leastone of Z to each be present.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purposes ofillustration and are not intended to be limiting, with the true scopeand spirit being indicated by the following claims.

What is claimed is:
 1. A method of detecting an improvement in a seizurecondition of a patient, the method comprising: identifying a first EEGsynchronization of a first seizure of the patient; applying a therapyconfigured to improve the seizure condition of the patient; identifyinga second EEG synchronization of a second seizure of the patientsubsequent to application of the therapy, wherein an improvement of theseizure condition of the patient is demonstrated by a reduced EEGsynchronization of the patient such that the second EEG synchronizationis less than the first EEG synchronization; and modifying the therapybased on at least one of the second EEG synchronization or a comparisonof the first and second EEG synchronization.
 2. The method of claim 1,wherein the therapy is at least one of a vagus nerve stimulation therapyor a drug therapy.
 3. The method of claim 1, further comprising:identifying a first amount of heart rate increase of the seizurecondition of the patient; identifying a second amount of heart rateincrease of the seizure condition of the patient subsequent toapplication of the therapy, wherein an improvement of the seizurecondition of the patient is demonstrated by a reduced increase in heartrate of the patient wherein the second amount of heart rate increase isless than the first amount of heart rate increase.
 4. The method ofclaim 1, further comprising: identifying a first duration of increasedheart rate of the seizure condition of the patient; identifying a secondduration of increased heart rate of the seizure condition of the patientsubsequent to application of the therapy, wherein an improvement of theseizure condition of the patient is demonstrated by a reduced durationof increased heart rate wherein the second duration of increased heartrate is less than the first duration of increased heart rate.
 5. Themethod of claim 1, wherein the first and second EEG synchronization aredetermined for a plurality of EEG sensors and a plurality of frequencybands for each of the plurality of EEG sensors.
 6. The method of claim1, wherein identifying the first EEG synchronization and identifying thesecond EEG synchronization each comprise: extracting maximum waveletcoefficients in a plurality of epochs and a plurality of frequency bandsfor a plurality of EEG sensors; computing a global spatialsynchronization for each frequency band across the plurality of EEGsensors using the maximum wavelet coefficients; and estimating asynchronizability index for each of the plurality of EEG sensors usingthe global spatial synchronization.
 7. The method of claim 6, furthercomprising, for each of the plurality of EEG sensors, classifying theEEG sensors as contributing to synchronization in response todetermining a significance for each of the plurality of EEG sensorsbased on the synchronizability index, wherein the significance indicatesan increase in the sychronizability index during the seizure.
 8. Themethod of claim 7, further comprising determining a number of EEGsensors contributing to seizure from the plurality of EEG sensors basedon the significance for each of the plurality of EEG sensors.
 9. Themethod of claim 8, further comprising evaluating an efficacy oftreatment by comparing the number of EEG sensors contributing to seizurein the first EEG synchronization to the number of EEG sensorscontributing to seizure in the second EEG synchronization.
 10. Themethod of claim 1, further comprising providing a visual representationof at least one of the first EEG synchronization, the second EEGsynchronization, or a comparison of the first and second EEGsynchronization to at least one of the patient or a physician.
 11. Themethod of claim 1, further comprising generating an indication of anefficacy of the therapy using at least one of the second EEGsynchronization or the comparison of the second EEG synchronization tothe first EEG synchronization.
 12. A device configured to detect animprovement in a seizure condition of a patient, the device comprising:at least one EEG sensor configured to generate EEG data; and a therapyanalysis device configured to receive the EEG data, wherein the therapyanalysis device is configured to evaluate an effect of a therapy, theevaluation comprising: identifying a first EEG synchronization of theseizure condition of the patient; applying a therapy configured toimprove the seizure condition of the patient; identifying a second EEGsynchronization of the seizure condition of the patient subsequent toapplication of the therapy, wherein an improvement of the seizurecondition of the patient is demonstrated by a reduced EEGsynchronization of the patient such that the second EEG synchronizationis less than the first EEG synchronization; and modifying the therapybased on at least one of the second EEG synchronization or a comparisonof the first and second EEG synchronization.
 13. The device of claim 12,wherein the therapy is at least one of a vagus nerve stimulation therapyor a drug therapy.
 14. The device of claim 12, further comprising atleast one heart rate sensor.
 15. The device of claim 14, wherein theevaluation further comprises: identifying a first amount of heart rateincrease of the seizure condition of the patient; identifying a secondamount of heart rate increase of the seizure condition of the patientsubsequent to application of the therapy, wherein an improvement of theseizure condition of the patient is demonstrated by a reduced increasein heart rate of the patient wherein the second amount of heart rateincrease is less than the first amount of heart rate increase.
 16. Thedevice of claim 14, wherein the evaluation further comprises:identifying a first duration of increased heart rate of the seizurecondition of the patient; identifying a second duration of increasedheart rate of the seizure condition of the patient subsequent toapplication of the therapy, wherein an improvement of the seizurecondition of the patient is demonstrated by a reduced duration ofincreased heart rate wherein the second duration of increased heart rateis less than the first duration of increased heart rate.
 17. The deviceof claim 12, wherein the first and second EEG synchronization aredetermined for the at least one EEG sensor and a plurality of frequencybands for the at least one EEG sensor.
 18. The device of claim 12,wherein identifying the first EEG synchronization and identifying thesecond EEG synchronization each comprise: extracting maximum waveletcoefficients in a plurality of epochs and a plurality of frequency bandsfor a plurality of EEG sensors; computing a global spatialsynchronization for each frequency band across the plurality of EEGsensors using the maximum wavelet coefficients; and estimating asynchronizability index for each of the plurality of EEG sensors usingthe global spatial synchronization.
 19. The device of claim 18, whereinidentifying the first EEG synchronization and identifying the second EEGsynchronization each further comprises, for each of the plurality of EEGsensors, classifying the EEG sensors as contributing to synchronizationin response to determining a significance for each of the plurality ofEEG sensors based on the synchronizability index, wherein thesignificance indicates an increase in the sychronizability index duringthe seizure.
 20. The device of claim 19, wherein identifying the firstEEG synchronization and identifying the second EEG synchronization eachfurther comprises determining a number of EEG sensors contributing toseizure from the plurality of EEG sensors based on the significance foreach of the plurality of EEG sensors.
 21. The device of claim 20, thetherapy analysis device is further configured to evaluate the effect ofa therapy by comparing the number of EEG sensors contributing to seizurein the first EEG synchronization to the number of EEG sensorscontributing to seizure in the second EEG synchronization.
 22. Thedevice of claim 12, the therapy analysis device is further configured toprovide a visual representation of at least one of the first EEGsynchronization, the second EEG synchronization, or a comparison of thefirst and second EEG synchronization to at least one of the patient or aphysician.
 23. The device of claim 12, wherein the therapy analysisdevice is configured to generate an indication of an efficacy of thetherapy using at least one of the second EEG synchronization or thecomparison of the second EEG synchronization to the first EEGsynchronization.
 24. A method of detecting an improvement in a seizurecondition of a patient, the method comprising: identifying a first EEGsynchronization of the seizure condition of the patient by: extractingfirst maximum wavelet coefficients in a first plurality of epochs and aplurality of frequency bands for a plurality of EEG sensors; computing afirst global spatial synchronization for each frequency band across theplurality of EEG sensors using the first maximum wavelet coefficients;and estimating a first synchronizability index for each of the pluralityof EEG sensors using the first global spatial synchronization; applyingstimulation to a vagus nerve of the patient using a vagus nervestimulation device configured to improve the seizure condition of thepatient; identifying a second EEG synchronization of the seizurecondition of the patient subsequent to application of the stimulationby: extracting second maximum wavelet coefficients in a second pluralityof epochs and the plurality of frequency bands for the plurality of EEGsensors; computing a second global spatial synchronization for eachfrequency band across the plurality of EEG sensors using the secondmaximum wavelet coefficients; and estimating a second synchronizabilityindex for each of the plurality of EEG sensors using the second globalspatial synchronization; and generating an indication of an efficacy ofthe stimulation to the vagus nerve using at least one of the secondsynchronizability index or a comparison of the second synchronizabilityindex to the first synchronizability index.
 25. The method of claim 24,further comprising: identifying a first amount of heart rate increase ofthe seizure condition of the patient; identifying a second amount ofheart rate increase of the seizure condition of the patient subsequentto application of the therapy, wherein an improvement of the seizurecondition of the patient is demonstrated by a reduced increase in heartrate of the patient wherein the second amount of heart rate increase isless than the first amount of heart rate increase.
 26. The method ofclaim 24, further comprising: identifying a first duration of increasedheart rate of the seizure condition of the patient; identifying a secondduration of increased heart rate of the seizure condition of the patientsubsequent to application of the therapy, wherein an improvement of theseizure condition of the patient is demonstrated by a reduced durationof increased heart rate wherein the second duration of increased heartrate is less than the first duration of increased heart rate.
 27. Themethod of claim 24, further comprising for each of the plurality of EEGsensors, classifying the EEG sensors as contributing to synchronizationin response to determining a first significance for each of theplurality of EEG sensors based on the first synchronizability index anddetermining a second significance for each of the plurality of EEGsensors based on the second synchronizability index, wherein the firstand second significance indicate a statistically significant increase inthe first and second sychronizability index, respectively, during theseizure.
 28. The method of claim 27, further comprising determining afirst number of EEG sensors contributing to seizure from the pluralityof EEG sensors based on the first significance for each of the pluralityof EEG sensors and a second number of EEG sensors contributing toseizure from the plurality of EEG sensors based on the secondsignificance for each of the plurality of EEG sensors.
 29. The method ofclaim 28, further comprising evaluating an efficacy of treatment bycomparing the first number of EEG sensors contributing to seizure in thefirst EEG synchronization to the second number of EEG sensorscontributing to seizure in the second EEG synchronization.
 30. Themethod of claim 24, further comprising modifying the therapy based on atleast one of the second EEG synchronization or a comparison of the firstand second EEG synchronization.
 31. The method of claim 24, furthercomprising providing a visual representation of at least one of thefirst EEG synchronization, the second EEG synchronization, or acomparison of the first and second EEG synchronization to at least oneof the patient or a physician.